首页|Infra-red spectroscopy combined with machine learning algorithms enables early determination of Pseudomonas aeruginosa's susceptibility to antibiotics

Infra-red spectroscopy combined with machine learning algorithms enables early determination of Pseudomonas aeruginosa's susceptibility to antibiotics

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  • NSTL
  • Elsevier
Pseudomonas (P.) aeruginosa is a bacterium responsible for severe infections that have become a real concern in hospital environments. Nosocomial infections caused by P. aeruginosa are often hard to treat because of its intrinsic resistance and remarkable ability to acquire further resistance mechanisms to multiple groups of antimicrobial agents. Thus, rapid determination of the susceptibility of P. aeruginosa isolates to antibiotics is crucial for effective treatment. The current methods used for susceptibility determination are time-consuming; hence the importance of developing a new method. Fourier-transform infra-red (FTIR) spectroscopy is known as a rapid and sensitive diagnostic tool, with the ability to detect minor abnormal molecular changes including those associated with the development of antibiotic- resistant bacteria. The main goal of this study is to evaluate the potential of FTIR spectroscopy together with machine learning algorithms, to determine the susceptibility of P. aeruginosa to different antibiotics in a time span of similar to 20 min after the first culture. For this goal, 590 isolates of P. aeruginosa, obtained from different infection sites of various patients, were measured by FTIR spectroscopy and analyzed by machine learning algorithms. We have successfully determined the susceptibility of P. aeruginosa to various antibiotics with an accuracy of 82-90%. (C) 2022 Elsevier B.V. All rights reserved.

Pseudomonas aeruginosaAntibioticsAntibiotic resistanceBacterial susceptibility to antibioticsInfra-red microscopyMachine learningFT-IR SPECTROSCOPYMULTIVARIATE-ANALYSISMOLECULAR-MECHANISMSMASS-SPECTROMETRYIDENTIFICATIONMICROORGANISMSTOOLRESISTANCEDIAGNOSISSAMPLES

Abu-Aqil, George、Suleiman, Manal、Sharaha, Uraib、Riesenberg, Klaris、Lapidot, Itshak、Salman, Ahmad、Huleihel, Mahmoud

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Ben Gurion Univ Negev

SCE Shamoon Coll Engn

Afeka Tel Aviv Acad Coll Engn

2022

10.1016/j.saa.2022.121080

Spectrochimica acta

Spectrochimica acta

ISSN：1386-1425
年,卷(期)：2022.274
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